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A New Statistical Downscaling Scheme for Predicting Winter Precipitation in China |
LIU Ying1,2, FAN Ke2, YAN Yu-Ping1 |
1Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China
2Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China |
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Abstract An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter (December-January-February) precipitation over China. The variable geopotential height at 500 hPa (GH5) over East Asia, which was obtained from National Centers for Environmental Prediction’s Coupled Forecast System (NCEP CFS), was used as one predictor for the scheme. The preceding sea ice concentration (SIC) signal obtained from observed data over high latitudes of the Northern Hemisphere was chosen as an additional predictor. This downscaling scheme showed significantly improvement in predictability over the original CFS general circulation model (GCM) output in cross validation. The multi-year average spatial anomaly correlation coefficient increased from -0.03 to 0.31, and the downscaling temporal root-mean-square-error (RMSE) decreased significantly over that of the original CFS GCM for most China stations. Furthermore, large precipitation anomaly centers were reproduced with greater accuracy in the downscaling scheme than those in the original CFS GCM, and the anomaly correlation coefficient between the observation and downscaling results reached ~ 0.6 in the winter of 2008.
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Received: 17 January 2013
Revised: 30 January 2013
Accepted: 01 February 2013
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Corresponding Author:
YAN Yu-Ping
E-mail: yanyp@cma.gov.cn
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